Building a Digital Respiratory Disease Framework for COPD management in Central Appalachia

项目摘要/摘要 与城市居民相比，农村社区慢性阻塞性肺病的发病率更高。 慢性阻塞性肺病(COPD)的疾病控制较差，导致发病率和死亡率较高。总的来说，农村居民 遭受更高的贫困和未参保率，再加上地理上的孤立和有限的机会 为了高质量的医疗保健。这一因素和其他社会因素导致了更高的发病率和更差的结果 几乎每一种慢性病。远程医疗承诺应对这些挑战，并扩大 这些社区的专科医生护理，但定性远程医疗访问通常缺乏必要的患者数据 医生做出明智的管理决策。远程医疗也常常受到有限的宽带的阻碍 在农村地区接入。用于远程监控的可穿戴技术也受到类似的限制，而且由于成本的原因 以及繁重的日常参与要求，导致老年、多病人口的遵从率较低。 为了应对这些挑战，Medentum正在开发一种负担得起、方便使用的多功能家用 配备摄像头、低成本传感器(温度、脉搏血氧计)和数字听诊器的设备。一个 配套的手机应用程序允许记录人口统计数据、社会文化数据、病史和 症状，并引导患者用手持设备收集他们的生物识别读数。如果没有 由于需要宽带，患者可以将他们的信息安全地传输到远程医生/慢性病 能够制定诊断和治疗计划的专家。对于这一SBIR，Medentum将改装其设备并 呼吸系统疾病的软件平台，用人工智能(AI)算法将其分层，创建数字 支持COPD自我管理的呼吸系统疾病框架(DRDF)。这项SBIR的目的是 1)研究这种COPD呼吸框架在阿巴拉契亚中部农村人口中的可用性和可行性 在弗吉尼亚州西南部的75名患者中，2)建立了自主预测COPD的初步人工智能算法 通过分析低负担变量，包括风险因素(社会、行为、环境)， 症状、COPD评估测试和设备生物特征(体温、脉搏、血氧饱和度、呼吸 心率和呼吸音)。在第二阶段，该平台将结合智能触发器，在某些情况下向患者发出警报 满足环境风险因素，促使他们参与平台。然后，智能算法将 自动预测他们的COPD恶化风险，最终将他们分流到适当的治疗。预测 并通过促进与医疗提供者的早期连接以及时和 有效的治疗将对农村COPD的健康结果和治疗费用产生巨大影响 病人。最终，这一平台将加强COPD自我管理，促进预防性呼吸护理， 和传播基于证据的COPD治疗，所有这些都旨在减少 偏远、服务不足的阿巴拉契亚中部人口的呼吸结果更差。

PROJECT SUMMARY/ABSTRACT Compared to urban residents, rural communities have higher rates of chronic obstructive pulmonary disease (COPD) with worse disease control, resulting in higher rates of morbidity and mortality. In general, rural residents suffer from higher poverty and uninsured rates, further compounded by geographic isolation and limited access to quality healthcare. This and other social factors have resulted in higher incidence and worse outcomes of nearly every chronic disease. Telehealth holds promise to address these challenges and expand access to specialist care in these communities, but qualitative telehealth visits often lack necessary patient data for physicians to make informed management decisions. Telemedicine is also often hindered by limited broadband access in rural areas. Wearable technologies for remote monitoring suffer similar limitations, and, due to cost and burdensome daily engagement requirements, lead to poor adherence rates in an older, sicker population. To address these challenges, Medentum is developing an affordable and accessible, multi-functional, home-use device featuring a camera, low-cost sensors (temperature, pulse oximeter) and a digital stethoscope. A companion phone application allows the recording of demographics, sociocultural data, medical history, and symptoms, and guides the patient to collect their biometric readings with the handheld device. Without the necessity of broadband, the patient can transmit their information securely to a remote physician/chronic disease specialist who can make a diagnosis and treatment plan. For this SBIR, Medentum will adapt its device and software platform for respiratory disease, layering it with artificial intelligence (AI) algorithms, to create a digital respiratory disease framework (DRDF) that empowers self-management of COPD. The aims of this SBIR are to 1) study the usability and feasibility of this COPD respiratory framework in a rural Central Appalachian population of 75 patients in Southwest Virginia and 2) build preliminary AI algorithms that autonomously predict COPD exacerbation risk by analyzing low burden variables including risk factors (social, behavioral, environmental), symptoms, COPD Assessment tests, and device biometrics (temperature, pulse, oxygen saturation, respiration rate and breath sounds). In Phase II, the platform will incorporate smart triggers that will alert patients if certain environmental risk factors are met, prompting them to engage with the platform. The smart algorithms will then automatically predict their COPD exacerbation risk, ultimately triaging them to appropriate treatment. Predicting and pre-empting disease exacerbation by facilitating early connections to medical providers for prompt and effective treatment will have an enormous impact on health outcomes and treatment costs for rural COPD patients. Ultimately, this platform will augment COPD self-management, promote preventive respiratory care, and disseminate evidence-based COPD treatments, all designed to reduce the significant health disparities in a remote, underserved Central Appalachian population with worse respiratory outcomes.